TU‐D‐207B‐02: Delta‐Radiomics: The Prognostic Value of Therapy‐Induced Changes in Radiomics Features for Stage III Non‐Small Cell Lung Cancer Patients. Issue 6 (30th November 2016)

• Record Type: Journal Article
• Authors: Fave, X; Zhang, L; Yang, J; Mackin, D; Stingo, F; Followill, D; Balter, P; Jones, A; Gomez, D; Court, L
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)
• Page Start: 3750
• Page End: 3750
• Publication Date: 2016-11-30
• Subjects: Cancer -- Medical image smoothing -- Dosimetry -- Impact testing -- Biomedical modeling -- Chemotherapy -- Computed tomography -- Lungs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4957510
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta‐radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co‐occurrence, run‐length, gray‐tone difference, and shape features, were calculated from pretreatment and weekly intra‐treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit‐depth resample and/or a smoothing filter were tested for their impact on volume‐correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized features were calculated from the intratreatment images and tested in linear mixed‐effects models to determine which features changed significantly with dose‐fraction. The slopes in these significant features were defined as delta‐radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta‐radiomics features for overall‐survival, local‐recurrence, and distant‐metastases. Leave‐one‐out cross validation was used for model‐fitting and patient predictions. Concordance indices(c‐index) and p‐values for the log‐rank test with patients stratified at the median were calculated. Results: Approximately one‐half of the 62 optimized features required no preprocessing, one‐fourth required smoothing, and one‐fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall‐survival, the c‐index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta‐radiomics features. For distant‐metastases, the c‐index improved from 0.53 to 0.58, while for local‐recurrence it did not improve. Patient stratification significantly improved (p‐value<0.05) for overallsurvival and distant‐metastases when delta‐radiomics features were included. The delta‐radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave‐one‐out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High‐risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).